海洋观监测传感器网络多接入边缘计算卸载方法

doi:10.11959/j.issn.2096-3750.2021.00205

物联网学报 ›› 2021, Vol. 5 ›› Issue (1): 36-52.doi: 10.11959/j.issn.2096-3750.2021.00205

所属专题：边缘计算

• 专题：物联网边缘智能与雾计算 • 上一篇下一篇

海洋观监测传感器网络多接入边缘计算卸载方法

苏新¹, 王子怡¹, 王宇鹏², 周思源³

¹ 河海大学物联网工程学院，江苏常州 213002
² 沈阳航空航天大学电子信息工程学院，辽宁沈阳 110136
³ 河海大学计算机与信息学院，江苏南京 211106

修回日期:2021-01-26 出版日期:2021-03-30 发布日期:2021-03-01
作者简介:苏新（1986- ），男，博士，河海大学副教授、硕士生导师，主要研究方向为移动通信、边缘/雾计算、智慧海洋等
王子怡（1997-），女，河海大学硕士生，主要研究方向为海洋网络、边缘/雾计算、计算卸载等
王宇鹏（1981-），男，博士，沈阳航空航天大学教授，辽宁省空天信息感知与智能处理重点实验室副主任，主要研究方向为移动通信技术、物联网、自组织网络等
周思源（1985-），男，博士，河海大学副教授，河海大学无线通信与智能系统研究所副所长，主要研究方向为车路协同自动驾驶、多天线通信技术、边缘计算等
基金资助:
国家自然科学基金资助项目(61801166);中央高校基本科研业务费资助项目(B210202091);中国民用航空局2019年度民航安全监管能力建设项目

Multi-access edge computing offloading in maritime monitoring sensor networks

Xin SU¹, Ziyi WANG¹, Yupeng WANG², Siyuan ZHOU³

¹ College of Internet of Things Engineering, Hohai University, Changzhou 213002, China
² College of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China
³ College of Computer and Information, Hohai University, Nanjing 211106, China

Revised:2021-01-26 Online:2021-03-30 Published:2021-03-01
Supported by:
The National Natural Science Foundation of China(61801166);The Fundamental Research Funds for the Central Universities(B210202091);The Safety Enhancement Construction Research Fund of Civil Aviation Administration of China

摘要/Abstract

摘要：

多接入边缘计算（MAC, multi-access edge computing）可有效保障海洋观监测传感器网络（简称传感网）的低时延、高可靠数据传输及其各类相关海事应用。在近海场景下，结合边缘计算资源分布建立多用户单跳单播（MSU, multi-user single-hop unicast）与多用户多跳单播（MMU, multi-user multi-hop unicast）两种卸载模型。利用混合整数非线性规划分离优化目标，有效地分配传输功率，并通过改进传统人工鱼群算法（AFSA, artificial fish swarm algorithm）制定卸载决策。结果表明，相比传统方案，所提优化算法可降低网络时延近19%。在远海场景下，建立远海MSU卸载模型，结合网络连通概率提出合理的信道分配算法。结果表明，所提算法在网络连通时间充足时，可增加允许分配子信道数量，降低网络时延；在网络连通时间有限时，可控制卸载的海洋用户设备数量，保障网络传输时延。

关键词: 海洋观监测传感器网络, 多接入边缘计算, 人工鱼群算法, 信道分配

Abstract:

Multi-access edge computing can effectively guarantee the low-latency, high-reliability data transmission of ocean monitoring sensor networks and various related maritime applications.In the offshore scenario, two offloading models of multi-user single-hop unicast and multi-user multi-hop unicast were established in combination with the distribution of edge computing resources.The mixed integer nonlinear programming was used to separate optimization targets and effectively allocate transmission power.The unloading decisions were made by improving the traditional artificial fish swarms algorithm.The results show that the proposed optimization algorithm can reduce the network delay by nearly 19% compared with the traditional scheme.In the far-sea scenario, a multi-user single-hop unicast offloading model was established, and a reasonable channel allocation algorithm was proposed based on the network connection probability.The results show that when the network connection time is sufficient, the number of allowable sub-channels can be increased to reduce the network delay.When the network connection time is limited, the number of unloaded marine user equipment can be controlled to ensure the network transmission delay.

Key words: maritime monitoring sensor network, multi-access edge computing, artificial fish swarm algorithm, channel allocation

中图分类号:

TN929.52

苏新, 王子怡, 王宇鹏, 周思源. 海洋观监测传感器网络多接入边缘计算卸载方法[J]. 物联网学报, 2021, 5(1): 36-52.

Xin SU, Ziyi WANG, Yupeng WANG, Siyuan ZHOU. Multi-access edge computing offloading in maritime monitoring sensor networks[J]. Chinese Journal on Internet of Things, 2021, 5(1): 36-52.

图/表 11

图1

表1

表2

近海MSU方案参数取值"

参数	取值
平均计算密度 $a$	[15, 25]cycles/bit
SBS计算能力 $f_{c}$	[20, 30]GHz
子任务最大能耗E_max	0.2 J
子任务额定功率 $p_{\max}$	[0.5, 1]W
OUE计算能力 $f l$	[0.05, 0.1]GHz
子信道带宽 $B$	[1, 2]MHz
信道增益噪声比 $\frac{g}{σ^{2}}$	[10, 200]W^-1

表2

图2

图3

表3

近海MMU方案参数取值"

参数	取值
平均计算密度 $\tilde{a}$	[5, 20]cycles/bit
SBS计算能力 ${\tilde{f}}_{c}$	50 GHz
子任务最大能耗 ${\tilde{E}}_{max}$	0.2 J
子任务额定功率 ${\tilde{p}}_{max}$	[0.5, 1]W
移动节点计算能力 ${\tilde{f}}_{m}$	[1, 15]GHz
OUE计算能力 $\tilde{f} l$	[0.01, 0.1]GHz
子信道带宽 $\tilde{B}$	[1, 2]MHz
信道增益噪声比 $\frac{\tilde{g}}{{\tilde{σ}}^{2}}$	[10, 200]W^{- 1}
任务数据量 $\tilde{D}$	[500, 700]KB
移动节点到固定节点的传输时延tts	0.008 s

表3

图4

图5

表4

远海MSU方案参数取值"

参数	取值
平均计算密度 $\tilde{a}$	[5, 15]cycles/bit
芯片结构参数 $\bar{l}$	10^-26
OUE最大能耗 ${\bar{E}}_{max}$	0.2 J
传输功率 $\bar{p}$	$[\frac{0.5}{\bar{N}}, \frac{1}{\bar{N}}] W$
移动节点计算能力 ${\bar{f}}_{c}$	[5, 8]GHz
本地最大计算速率 $f {\bar{l}}_{max}$	[0.01, 0.1]GHz
子信道带宽 $\bar{B}$	1 MHz
信道增益噪声比 $\frac{\bar{g}}{{\bar{σ}}^{2}}$	[10, 200]W^{- 1}
任务数据量 $\bar{D}$	[500, 700]KB
处理1 bit数据返回数据比β	0.3

表4

图6

图7

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AFSA	近海MSU卸载决策优化问题
人工鱼的位置	卸载决策向量
解空间	卸载决策的取值范围
适应度函数	优化目标函数

海洋观监测传感器网络多接入边缘计算卸载方法

Multi-access edge computing offloading in maritime monitoring sensor networks

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